Matt Spangler, Bruce Golden, Larry Kuehn, Mark Thallman, Warren Snelling, Bob Weaber
▪ The global Animal Breeding and Genetics community has done a tremendous job at increasing scientific knowledge, developing selection tools, and delivering these tools to the US Beef Industry. ▪ Despite these advancements, technology adoption is embarrassingly poor. ▪ < 30% of producers use EPD (Weaber et al., 2014)
▪ Poor technology adoption is related to the sum of many underlying issues: ▪ Genetic prediction seems opaque ▪ Consultancy is often from sources other than what might be preferred ▪ Commercial producers do not have the needed time to excel in all areas, and focus on day-to-day animal and financial management ▪ Combining all partial solutions is a very cumbersome task ▪ Breeding objective ▪ Breeding system ▪ Breed choice ▪ Trait emphasis ▪ Sire selection ▪ And all need to contemplate that which is economical and possible given environmental constraints
▪ USDA Funded CARE Grant ▪ Aim is to develop a web-based tool to aid in genetic selection decisions ▪ Initiated with an industry-wide survey in 2018 ▪ Advisory board of producers (commercial and seedstock), extension faculty, breed association staff 4
▪ Online Survey of Beef Producers ▪ Fall/winter 2018-2019 ▪ 1,530 respondents ▪ Self selected ▪ Nationally publicized (Breed Assn., NCBA, Extension lists, etc.) ▪ 1,161 completed survey 5
100 90 80 Percent of Respondents 70 60 50 40 30 20 10 0 Owner Employee Manager 6
7
Percent of Respondents 10 15 20 25 30 35 40 45 0 5 8
30 25 Percentage of Respondents 20 15 10 5 0 <25 25-50 51-100 101-250 251-500 501-1000 >1000 9
60 50 Percent of Respondents 40 30 20 10 0 0 1-2 3-5 6-10 11-20 20 or more N/A 10
45 40 35 Percent of Respondents 30 25 20 15 10 5 0 No Response Strongly Somewhat Neither agree Somewhat Strongly agree disagree disagree nor disagree agree 11
50 45 40 Percent of Respondents 35 30 25 20 15 10 5 0 No Response Strongly Somewhat Neither agree Somewhat Strongly agree disagree disagree nor disagree agree 12
60 50 Percent of Respondents 40 30 20 10 0 No Response Strongly Somewhat Neither agree Somewhat Strongly agree disagree disagree nor disagree agree 13
14
60 50 Percent of Respondents 40 30 20 10 0 No Response Strongly Somewhat Neither agree Somewhat Strongly agree disagree disagree nor disagree agree 15
80 70 60 Percent of Responsdents 50 40 30 20 10 0 No Response Not at all Slightly Moderately Very important Extremely important important important important 16
45 40 35 Percent of Respondents 30 25 20 15 10 5 0 No Response Not at all Slightly Moderately Very important Extremely important important important important 17
40 35 30 Percent of Respondents 25 20 15 10 5 0 No Response None Not detailed Somewhat Very detailed detailed 18
50 45 40 Percent of Respondents 35 30 25 20 15 10 5 0 No Response None Not detailed Somewhat Very detailed detailed 19
50 45 40 Percent of Respondents 35 30 25 20 15 10 5 0 No Daily Less than Never Once per Rarely Twice or Twice or Response once per week more daily more per week week 20
90 80 70 Percent of Respondents 60 50 40 30 20 10 0 No Response FALSE TRUE 21
45 40 35 Percent of Respondents 30 25 20 15 10 5 0 No Response Definitely not Probably not Might or might Probably yes Definitely yes not 22
45 40 35 Percent of Respondents 30 25 20 15 10 5 0 No Response Strongly Somewhat Neither agree Somewhat Strongly agree disagree disagree nor disagree agree 23
▪ Tool to enable informed multiple-trait selection ▪ Based on: ▪ Breeding objectives ▪ Economic parameters ▪ Relationships among traits ▪ Population (herd) means ▪ Designed to improve commercial level profitability
▪ Develop a Breeding Objective ▪ Identifies sources of cost and revenue ▪ Sets goals conditioned on resources ▪ Identify breed(s) ▪ Develop a Breeding System ▪ Select seedstock supplier(s) ▪ Select bulls ▪ Should align with breeding objective
Data Knowledge Requires turning data Data is constantly into tools growing This is where the global (more animals, more ABG community spends traits, more genotypes, a great deal of time sequence data)
▪ A lot of bull sales, and a lot of bulls in each sale ▪ Too many EPD — hard, if not impossible, to select on multiple traits simultaneously using only individual EPD ▪ In many cases EPD are breed-specific — must convert to common base ▪ Need to account for the value of heterosis and differences in breeds relative to average performance ▪ Indexes exist and are provided by breed associations (and some vendors) ▪ Although robust they are generalizations
Tools Decisions Requires turning tools into Increasing list of impactful EPD decisions
▪ Producers face the problem of obtaining the best bulls for their operation in that given setting. ▪ ‘Best’ is a relative concept. ▪ A ‘less desirable’ bull may become the preferred choice over a ‘more desirable’ bull if his sale price discount is larger than the differential in value between the two bulls.
▪ We have framed three possible use cases: ▪ Commercial buyers (genetic purchasing decisions based on firm-specific breeding objectives) ▪ Seedstock sellers (matching sale offering to individual customers) ▪ Seedstock buyers (matching genetic purchasing decisions to specified goals)
▪ (co)Variances — literature ▪ Cost/revenue pricing — industry averages or use- defined ▪ Breed information — user defined ▪ Phenotypic means — industry averages or user defined ▪ Breeding objectives — user defined ▪ EPD — Uploaded (user or seedstock seller), secure API breed association
Use case Breeding objective Herd-level parameters Identification of breeds/breeders Individual selection
▪ Tiered layer of input ▪ Essentially generalized index ▪ Reasonable knowledge of unit cost of production ▪ Discounted gene flow ▪ Discounted expression rates ▪ Planning horizon ▪ Can be used to create generalized indexes with ability to further “tweak” by members/users
▪ Alpha version with grant team ▪ Next steps ▪ Version to advisory board ▪ Key training sessions (extension personnel, breed association staff) 34
▪ The impetus for this project is not the belief that currently available selection indices are so inherently flawed that they are of little value. ▪ We believe that allowing beef cattle producers to take part in the creation of their own selection index has the potential to increase the rate of technology adoption. ▪ The other primary improvement is in the ability to combine multiple partial solutions (e.g., additive and non-additive genetic effects) to enable sire selection across breeds in an economic framework.
USDA-AFRI-CARE Beef Cattle Production System Decision Support Tools to Enable Improved Genetic, Environmental, and Economic Resource Management Survey of Industry Stakeholders; Award Number: 2018-68008-27888 36
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